Acme is an open-source library by DeepMind designed to streamline reinforcement learning research. It provides modular agent building blocks, configurable training loops, and integrated logging, enabling rapid experimentation and scalable distributed training across diverse environments.
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#reinforcement learning
#RL agents
#Machine Learning Framework
#DQN
#PPO
#SAC
#Distributed training
#scalable experiments
#algorithm prototyping
#TensorFlow integration
#JAX support
#environment wrappers
#replay buffers
#logging utilities
#hyperparameter tuning
#checkpointing
#metric tracking
#Research Tools
#Performance Benchmarking
#custom environments
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Acme
Acme is an open-source library by DeepMind designed to streamline reinforcement learning research. It provides modular agent building blocks, configurable training loops, and integrated logging, enabling rapid experimentation and scalable distributed training across diverse environments.
AI Platforms & Frameworks
Tool Libraries
#reinforcement learning
#RL agents
#Machine Learning Framework
#DQN
#PPO
#SAC
#Distributed training
#scalable experiments
#algorithm prototyping
#TensorFlow integration
#JAX support
#environment wrappers
#replay buffers
#logging utilities
#hyperparameter tuning
#checkpointing
#metric tracking
#Research Tools
#Performance Benchmarking
#custom environments
...
Featured
What is Acme?
Acme is a Python-based framework that simplifies the development and evaluation of reinforcement learning agents. It offers a collection of prebuilt agent implementations (e.g., DQN, PPO, SAC), environment wrappers, replay buffers, and distributed execution engines. Researchers can mix and match components to prototype new algorithms, monitor training metrics with built-in logging, and leverage scalable distributed pipelines for large-scale experiments. Acme integrates with TensorFlow and JAX, supports custom environments via OpenAI Gym interfaces, and includes utilities for checkpointing, evaluation, and hyperparameter configuration.
Who will use Acme?
- Reinforcement learning researchers
- Machine learning engineers
- Academic and industrial research labs
- Advanced Python developers
How to use the Acme?
- Step1: Clone the Acme GitHub repository and navigate to the project folder.
- Step2: Install dependencies via pip: `pip install acme dm-env dm-tree chex jax tensorflow`.
- Step3: Import Acme modules in your script: `import acme`.
- Step4: Define your environment using OpenAI Gym or dm_env.
- Step5: Choose or implement an agent from Acme’s library (e.g., DQN, PPO).
- Step6: Configure the training loop and hyperparameters.
- Step7: Run training with `acme.run_experiment()` or custom training script.
- Step8: Monitor metrics via TensorBoard and save checkpoints.
Platform
- mac
- windows
- linux
Acme's Core Features & Benefits
The Core Features
- Prebuilt agent implementations (DQN, PPO, SAC, etc.)
- Modular replay buffers and environment wrappers
- Configurable training loops and schedulers
- Distributed execution engine for scalable training
- Integrated logging and evaluation utilities
- TensorFlow and JAX compatibility
- Checkpointing and metric tracking
The Benefits
- Accelerates RL research with reusable components
- Simplifies prototyping of new algorithms
- Supports large-scale distributed experiments
- Enhances reproducibility and benchmarking
- Streamlines integration with custom environments
Acme's Main Use Cases & Applications
- Rapid prototyping of novel reinforcement learning algorithms
- Benchmarking standard RL agents on custom tasks
- Distributed training for large-scale RL research
- Educational demonstrations of RL concepts
- Hyperparameter tuning and automated evaluation
FAQs of Acme
Acme Company Information
- Google DeepMind
Acme Reviews
5/5
Acme's Main Competitors and alternatives?
- Ray RLlib
- TensorFlow Agents
- Stable Baselines3
- OpenAI Spinning Up
- Dopamine
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